Dr. Michelle Moyer, Washington State University’s statewide viticulture extension specialist, regularly receives calls from growers asking advice about planting wine grapes in the state. Using a new computer model designed by WSU to evaluate potential vineyard sites, she provides callers with specific site information to guide their decisions—all without ever leaving her office.

These days, budget limitations preclude county and state extension educators from visiting farms. Much of their expertise is shared digitally—through electronic newsletters, online webinars, and phone calls. “Nobody can travel all over the state anymore,” said Moyer. “But it’s still my role to help commercial grape growers. While nothing can replace an on-site, visual inspection, the WSU computer model is a great decision-making tool that can highlight the positives and negatives of a commercial vineyard site.”

Washington’s wine grape industry is going through a major expansion phase, and new vineyards are being planted in uncharted areas for viticulture. Moyer has fielded a lot of calls about sites in western Washington, but also locations in north central Washington, such as Douglas, Stevens, and Okanogan counties. She is charged with helping grape growers succeed in producing quality grapes, and to do that, the first step is to select an appropriate vineyard site.

Much of what determines the suitability of a vineyard site relates to soil. Factors like soil pH, texture, water-holding capacity and drainage, and the presence of restricting soil layers are key considerations when selecting a site. Some factors are topographical, such as slope, aspect, and elevation, while others are temperature based. The information is publicly available, but it is ­scattered among different government entities and ­databases.

Data and more data

Developing a vineyard site-selection computer model had long been on the wish list of WSU soil scientist Dr. Joan Davenport. When she saw Ian Yau’s application for WSU’s graduate school, his geographical information systems experience caught her eye. Davenport became Yau’s advisor, and funding was secured for his computer model project.

Yau spent four years compiling massive amounts of raw data into a single database for the computer model. Some of the data sets, like soil surveys, solar radiation, and topographical maps were readily available, while others, such as growing degree days, were incomplete or needed interpolation.

The second phase of the data-gathering process involved a literature search to weight the different datasets for their grape quality attributes, giving them a numerical rank, explained Moyer. The higher the number, the more appropriate the site is for grapes, she said while demonstrating to Good Fruit Grower how the model works. A zero means the site is inappropriate for a ­vineyard.

The model has two sets of data: raw data and classified. The weighted rankings are part of the classified data. By using both data sets, Moyer can identify why a site ranked low or high, not just that it ranked low.

Model limitations

Ideally, the model would work by using a mathematical calculation that combines multiple factors in a ranked form to determine the overall suitability of a site, she said. Moyer, who has experience in computer modeling, helped develop a powdery mildew risk assessment model at Cornell University, New York, before joining WSU.

“The original concept was to have the site model available to the public, similar to other WSU computer risk-assessment models, like those for powdery mildew and the Decision Aid System,” she said. “It was to be a tool for growers to use in their decision making.”

But it’s not ready for public use and is currently used only internally by WSU research and extension staff. Also, the model is set up to work in vineyards of commercial size—not small acreage parcels.

During field testing, the model was able to accurately assess in-season conditions that would be favorable or detrimental to grape production, such as heat accumulation and frost-free days. The model also provided excellent insight into site soil conditions and situations such as excess or insufficient drainage.

But a major limitation is that it can’t capture extreme weather events. “The model in its current form is not able to predict the likelihood of dormant freezing events, a critical factor in site selection in eastern Washington,” she stated in a report. “An additional data layer that would include the likelihood of dormant season low-temperature events is necessary to improve the model’s prediction of ‘no-grow’ sites.”

She also noted that some of the raw data is not detailed or current enough, and therefore is limited in value. For example, most of the soil surveys were conducted by the government decades ago and have not been updated. If the site has been farmed since the soil survey, soil amendments could have altered the soil pH or installation of tile drains could have changed drainage issues, for example.

Additionally, resolution of the topography maps is not high enough to provide adequate slope or air drainage information. And, the model does not factor in water availability or legal water rights, a key ingredient to grape growing.

Although the model itself didn’t turn out as originally envisioned, she says the database has proven to be a very useful tool for WSU Extension to use for off-location ­evaluation.

Evaluation not selection

She stressed that the model should be considered a site “evaluation” tool and not a site “selection” tool.

One of the early goals of the model was to be able to identify sites capable of producing “ultra premium” wine grapes. “But people are not part of the model’s site evaluation,” she said. “There are two things you can’t model—you can’t model the vineyard manager or the winemaker.”

To prove her point, she modeled a vineyard site in the state known for producing perfect or very high scoring wines. The computer gave the site a moderate score for vineyard appropriateness. “Great vineyards are about the people,” she said, adding that a good vineyard manager can make any site work, while a poor manager can make a good site not work.

“Agriculture is about risk,” said Moyer. “This model helps me pinpoint potential problems and challenges that a grower will encounter on a given site. It helps me direct the grower toward things he or she should be concerned about and is a way to help growers take ownership of the risks and make informed decisions.”